• Journal of the Korean Data and Information Science Society
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• v.28 no.6
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• pp.1457-1469
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• 2017

Private education expenses is one of the key issues in Korea and there have been many discussions about it. Academically, most of previous researches for private education expenses have used multiple regression linear model based on ordinary least squares (OLS) method. However, if the data do not satisfy the basic assumptions of the OLS method such as the normality and homoscedasticity, there is a problem with the reliability of estimations of parameters. In this case, quantile regression model is preferred to OLS model since it does not depend on the assumptions of nonnormality and heteroscedasticity for the data. In the present study, the data from a survey on private education expenses, conducted by Statistics Korea in 2015 has been analyzed for investigation of the impacting factors for private education expenses. Since the data do not satisfy the OLS assumptions, quantile regression model has been employed in Bayesian approach by using gibbs sampling method. The analysis results show that the gender of the student, parent's age, and the time and cost of participating after school are not significant. Household income is positively significant in proportion to the same size for all levels (quantiles) of private education expenses. Spending on private education in Seoul is higher than other regions and the regional difference grows as private education expenditure increases. Total time for private education and student's achievement have positive effect on the lower quantiles than the higher quantiles. Education level of father is positively significant for midium-high quantiles only, but education level of mother is for all but low quantiles. Participating after school is positively significant for the lower quantiles but EBS textbook cost is positively significant for the higher quantiles.

• Communications for Statistical Applications and Methods
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• v.19 no.2
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• pp.293-301
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• 2012

Quantile regression proposed by Koenker and Bassett (1978) is a statistical technique that estimates conditional quantiles. The advantage of using quantile regression is the robustness in response to large outliers compared to ordinary least squares(OLS) regression. A regression tree approach has been applied to OLS problems to fit flexible models. Loh (2002) proposed the GUIDE algorithm that has a negligible selection bias and relatively low computational cost. Quantile regression can be regarded as an analogue of OLS, therefore it can also be applied to GUIDE regression tree method. Chaudhuri and Loh (2002) proposed a nonparametric quantile regression method that blends key features of piecewise polynomial quantile regression and tree-structured regression based on adaptive recursive partitioning. Lee and Lee (2006) investigated wage determinants in the Korean labor market using the Korean Labor and Income Panel Study(KLIPS). Following Lee and Lee, we fit three kinds of quantile regression tree models to KLIPS data with respect to the quantiles, 0.05, 0.2, 0.5, 0.8, and 0.95. Among the three models, multiple linear piecewise quantile regression model forms the shortest tree structure, while the piecewise constant quantile regression model has a deeper tree structure with more terminal nodes in general. Age, gender, marriage status, and education seem to be the determinants of the wage level throughout the quantiles; in addition, education experience appears as the important determinant of the wage level in the highly paid group.

• The Korean Journal of Applied Statistics
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• v.21 no.1
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• pp.1-17
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• 2008

The ordinary least squares regression method of Haseman and Elston(1972) is most widely used in genetic linkage studies for continuous traits of sib pairs. Kruglyak and Lander(1995) suggested a statistic which appears to be a nonparametric counterpart to the Haseman and Elston(1972)'s regression method, but in fact these two methods are quite different. In this paper the relationships between these two methods are described and will be compared by simulation studies. One of the characteristics of the sib-pair linkage study is that the explanatory variable has only three different values and thus dependent variable is heavily replicated in each value of the explanatory variable. We propose a weighted least squares regression method which is more appropriate to this situation and the efficiency of the weighted regression in genetic linkage study was explored with normal and non-normal simulated continuous traits data. Simulation studies demonstrated that the weighted regression is more powerful than other tests.

• The Journal of Economics, Marketing and Management
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• v.5 no.1
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• pp.27-37
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• 2017

Domestic government debt securities is one of the steps which is taken by the government of Indonesia as a major source of financial budget, covering for the budget deficit, debt payments and interest debt. The purposes of this research are to know the development of budget deficits, government debt and impact of domestic government debt securities against economic growth in Indonesia. Method of analysis used Ordinary Least Squares (OLS) analyzing the impact of the domestic debt against economic growth in Indonesia. This research uses time series data from 1997 to 2014. Total government debt and domestic government debt securities in Indonesia increased during the last five years. The average of domestic government securities was above 50 percent of the total government debt. Estimated results showed domestic government debt securities has a positive and significant effect to economic growth. Official development assistance (ODA) has a negative effect to economic growth. Other variables such as the gross fixed capital formation and receipt of remittance have positive and significant effect, total imports and government expenditure have negative and significant effect against economic growth.

• Journal of the Korea Society for Simulation
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• v.23 no.3
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• pp.11-17
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• 2014

The central composite design (CCD) is often used to estimate the second-order linear model. This paper uses a correlation induction strategy of common random numbers (CRN) in simulation experiment and utilizes the induced correlations to obtain better estimates for the second-order linear model. This strategy assigns the CRN to all design points in the CCD. An appropriate selection of the axial points in CCD makes the weighted least squares (WLS) estimator be equivalent to ordinary least squares (OLS) estimator in estimating the linear model parameters of CCD. We analytically investigate the efficiency of this strategy in estimation of model parameters. Under certain conditions, this correlation induction strategy yields better results than independent random number strategy in estimating model parameters except intercept. The simulation experiment on a selected model supports such results. We expect a suggested random number assignment is useful in application of CCD in simulation experiments.

• The Journal of Fisheries Business Administration
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• v.42 no.2
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• pp.69-83
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• 2011

This paper estimates demand functions of oyster as Kimchi's ingredients of capital area, other areas excluding a capital area, and a whole area in Korea to forecast its demand quantities in 2011~2015. To estimate oyster demand function, this paper uses pooled data produced from Korean housewives over 30 years old in 2009 and 2010. Also, this paper adopts several econometrics methods such as Ordinary Least Squares and Feasible Generalized Least Squares. First of all, to choose appropriate variables of oyster demand functions by area, this paper carries out model's specification with joint significance test. Secondly, to remedy heteroscedasticity with pooled data, this paper attempts residual plotting between estimated squared residuals and estimated dependent variable and then, if it happens, undertakes White test to care the problem. Thirdly, to test multicollinearity between variables with pooled data, this paper checks correlations between variables by area. In this analysis, oyster demand functions of a capital area and a whole area need price of the oyster, price of the cabbage for Gimjang, and income as independent variables. The function on other areas excluding a capital area only needs price of the oyster and income as ones. In addition, the oyster demand function of a whole area needed White test to care a heteroscedasticity problem and demand functions of the other two regions did not have the problem. Thus, first model was estimated by FGLS and second two models were carried out by OLS. The results suggest that oyster demand quantities per a household as Kimchi's ingredients are going to slightly increase in a capital area and a whole area, but slightly decrease in other areas excluding a capital area in 2011~2015. Also, the results show that oyster demand quantities as kimchi's ingredients for total household targeting housewives over 30 years old are going to slightly increase in three areas in 2011~2015.

• Asian Journal of Business Environment
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• v.10 no.2
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• pp.23-31
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• 2020

Purpose: The purpose of this paper is to find out the impact of financial leverage on firm's profitability in the listed textile sector of Bangladesh. Research design, data and methodology: A sample of 22 DSE listed textile firms has been used to conduct the study. In this study, firm profitability is measured by Return on Equity (ROE) and both short term debt and long term debt are used as the as proxies of financial leverage. Pooled Ordinary Least Squares (OLS), Fixed Effect (FE), and Generalized Method of Moments (GMM) models have been used to test the relationship between financial leverage and profitability of firms. Result: This study finds a significant negative relationship between leverage and firm's profitability using the Pooled OLS method. The result is also consistent with the fixed effect and GMM method. This result implies that firm's profitability is negatively affected by the firm's capital structure. Conclusion: The study concludes that maximum textile firms use external debt as a source of finance as they don't have sufficient internally generated funds. This study recommends that firm should give more emphasize on generating fund internally to meet up their financing needs.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.2
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• pp.1237-1246
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• 2021

The purpose of the research is to evaluate how education influences the income of household heads, who are young adult in rural Vietnam. In order to examine the impact of education on the households where their heads are young adults, in this paper, the authors employ two research methods. First, ordinary least squares (OLS) regression is used to study the impact of education on different groups of income; second, quantile regression is applied to find out how education influences the income of households. The dataset includes a survey of 800 young households aged between18 and 35 who are the head of agricultural farms in rural areas. The findings indicate that education has a positive impact on income of young households. Furthermore, the results prove that the longer schooling years, the higher income youth can attain. The results showed that, at the survey time (Sep 2019), the average monthly income of rural young adults who are joining the production process shows a big gap between low and high incomes. Moreover, the study has revealed that other factors positively affect the incomes, namely, joining job-related associations, land resource, hired labour, hi-tech application as well as extension of producing unit.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.10
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• pp.227-234
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• 2022

The proportion of people living in poverty in Soc Trang is comparatively large. 27,154 households in this province are considered to be poor, which represents 8.4 percent of all households. The gap between rural and urban areas, between farmers and other social classes in this province, tends to increase, and the living standard of people in the countryside remains difficult. This paper aims to investigate the determinant factors of poor households' income in rural areas of Soc Trang province, Vietnam. Data from 120 poor households in Vinh Chau district and Ke Sach district of Soc Trang province collected in the year 2019 is employed to test the proposed hypotheses in this study. By applying the descriptive statistical method and ordinary least squares (OLS) regression, the results show that the factors of production land, number of income generation activities, access to credit, means of transportation, and means of production positively affect the income per capita of poor household in the study area, whereas household size has a negative impact on the household income per capita. Considering the empirical findings, several solutions and recommendations are proposed to improve the income of poor households in Soc Trang province.

• New & Renewable Energy
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• v.17 no.3
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• pp.24-31
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• 2021

Through 'The Renewable Energy 3020 Implementation Plan' for carbon neutrality, the government promised to raise the proportion of renewable energy generation to 20% and renewable energy installation capacity to 63.8% by 2030. Therefore, we plan to test a 5.5 MW offshore wind turbine near Maldo, Gunsan. In this project, we measure the level of public acceptance and perform ordinary least squares (OLS) regression analysis to show the determinants of public acceptance. The regression results are as followed. First, it is judged that the closer the distance to the offshore wind turbine, the more the economic effects considered by residents. Second, especially in Maldo, the experience of being discriminated from the Saemangeum project, is understood to have caused distrust in the surrounding fishing villages chief/Fisheries Cooperatives, converted into a local community effect. Finally, the policy implications are as follows. First, a bottom-up problem-solving method is required to improve public acceptance, based on the Living Lab. Second, the island community may be indifferent to the briefings or forums of outsiders. Therefore, a gradual approach is required through (in)formal channels based on reliability from a long-term perspective with nearby universities and research institutes using SamsØ Energy Academy.